论文Word文件下载.docx
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论文Word文件下载.docx
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论文@#@1DefiningQuestions@#@Ascientificfieldisbestdefinedbythecentralquestionitstudies.ThefieldofMachineLearningseekstoanswerthequestion@#@“Howcanwebuildcomputersystemsthatautomaticallyimprovewithexperience,andwhatarethefundamentallawsthatgovernalllearningprocesses?
@#@”@#@Thisquestioncoversabroadrangeoflearningtasks,suchashowtodesignautonomousmobilerobotsthatlearntonavigatefromtheirownexperience,howtodataminehistoricalmedicalrecordstolearnwhichfuturepatientswillrespondbesttowhichtreatments,andhowtobuildsearchenginesthatautomaticallycustomizetotheiruser’sinterests.Tobemoreprecise,wesaythatamachinelearnswithrespecttoaparticulartaskT,performancemetricP,andtypeofexperienceE,ifthesystemreliablyimprovesitsperformancePattaskT,followingexperienceE.@#@DependingonhowwespecifyT,P,andE,thelearningtaskmightalsobecalledbynamessuchasdatamining,autonomousdiscovery,databaseupdating,programmingbyexample,etc.@#@MachineLearningisanaturaloutgrowthoftheintersectionofComputerScienceandStatistics.WemightsaythedefiningquestionofComputerScienceis“Howcanwebuildmachinesthatsolveproblems,andwhichproblemsareinherentlytractable/intractable?
@#@”ThequestionthatlargelydefinesStatisticsis“Whatcanbeinferredfromdataplusasetofmodelingassumptions,withwhatreliability?
@#@”ThedefiningquestionforMachineLearningbuildsonboth,butitisadistinctquestion.WhereasComputerSciencehasfocusedprimarilyonhowtomanuallyprogramcomputers,MachineLearningfocusesonthequestionof@#@howtogetcomputerstoprogramthemselves(fromexperienceplussomeinitialstructure).WhereasStatisticshasfocusedprimarilyonwhatconclusionscanbeinferredfromdata,MachineLearningincorporatesadditionalquestionsaboutwhatcomputationalarchitecturesandalgorithmscanbeusedtomosteffectivelycapture,store,index,retrieveandmergethesedata,howmultiplelearningsubtaskscanbeorchestratedinalargersystem,andquestionsofcomputationaltractability.@#@AthirdfieldwhosedefiningquestioniscloselyrelatedtoMachineLearningisthestudyofhumanandanimallearninginPsychology,Neuroscience,andrelatedfields.Thequestionsofhowcomputerscanlearnandhowanimalslearnmostprobablyhavehighlyintertwinedanswers.Todate,however,theinsightsMachineLearninghasgainedfromstudiesofHumanLearningaremuchweakerthanthoseithasgainedfromStatisticsandComputerScience,dueprimarilytotheweakstateofourunderstandingofHumanLearning.Nevertheless,thesynergybetweenstudiesofmachineandhumanlearningisgrowing,withmachinelearningalgorithmssuchastemporaldifferencelearningnowbeingsuggestedasexplanationsforneuralsignalsobservedinlearninganimals.OverthecomingyearsitisreasonabletoexpectthesynergybetweenstudiesofHumanLearningandMachineLearningtogrowsubstantially,astheyarecloseneighborsinthelandscapeofcorescientificquestions.@#@Otherfields,frombiologytoecomonicstocontroltheoryalsohaveacoreinterestinthequestionofhowsystemscanautomaticallyadaptoroptimizetotheirenvironment,andmachinelearningwilllikelyhaveanincreasingexchangeofideaswiththesefieldsoverthecomingyears.Forexample,economicsisinterestedinquestionssuchashowdistributedcollectionsofself-interestedindividualsmayformasystem(market)thatlearnspricesleadingtopareto-optimalallocationsforthegreatestcommongood.Andcontroltheory,especiallyadaptivecontroltheory,isinterestedinquestionssuchashowaservo-controlsystemcanimproveitscontrolstrategythroughexperience.Interestingly,themathematicalmodelsforadaptationintheseotherfieldsaresomewhatdifferentfromthosecommonlyusedinmachinelearning,suggestingsignificantpotentialforcross-fertilizationofmodelsandtheories.@#@ThefollowingsectionsdiscussthestateoftheartofMachineLearning,asampleofsuccessfulapplica-tions,andasampleofopenresearchquestions.@#@2StateofMachineLearning@#@Herewedescribesomeoftheprogressinmachinelearning,aswellasopenresearchquestions.@#@2.1ApplicationSuccesses@#@OnemeasureofprogressinMachineLearningisitssignificantreal-worldapplications,suchasthoselistedbelow.Althoughwenowtakemanyoftheseapplicationsforgranted,itisworthnotingthataslateas1985therewerealmostnocommercialapplicationsofmachinelearning.@#@*Speechrecognition.Currentlyavailablecommercialsystemsforspeechrecognitionallusemachinelearninginonefashionoranothertotrainthesystemtorecognizespeech.Thereasonissimple:
@#@thespeechrecognitionaccuracyisgreaterifonetrainsthesystem,thanifoneattemptstoprogramitbyhand.Infact,manycommercialspee
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